Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions

TLDR

Autonomous vehicles are a research focus due to safety, congestion, emissions, and mobility benefits, with motion‑planning algorithms central to software that must find safe, efficient paths while avoiding obstacles, and many diverse approaches exist. This paper reviews and compares motion‑planning methods for on‑road autonomous driving—path finding, safest manoeuvre selection, and trajectory planning—to identify challenges and propose future research directions that accelerate development. The authors critically evaluate methods across the three planning levels, assessing complexity, accuracy, advantages, limitations, feasibility, optimality, obstacle handling, and operational testing environments. Future work should leverage V2V/V2I communications and transport‑engineering insights to extend sensing horizons, thereby improving planning performance and reducing driverless vehicle costs.

Abstract

Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research.